PROJECT SUMMARY Autism is a behaviorally-defined diagnosis that affects approximately 1 in 59 children in the US. Recent genetic studies have revealed that autism is an umbrella term for a large family of individually rare, collectively common genetic disorders, with each individual gene accounting for only a small portion of total cases. This presents a problem for researchers attempting to develop biologically-based drug or treatment strategies: the autism diagnostic entity is too broad to be biologically meaningful, but most individual genetic mutations are too rare to allow for sufficient patient recruitment or for commercially viable drug development. There is an urgent unmet need to develop a subtyping strategy that can assign patients into one of a small number of biologically meaningful subtypes that might be amenable to targeted treatment strategies. We have recently developed a novel proteomic strategy that makes high-dimensional measurements of protein-protein interaction networks (PINs). These measures reflect several relevant features of autism pathogenesis- synaptic content, recent activity, and developmental stage of the neuron. We postulate that different genetic autisms converge on two specific PINs and produce patterns of network disruption that, while individually unique, share common features that will allow clustering of PIN matrices into subtypes. Importantly, our clustering methods allow identification of specific signal transduction nodes that define each sub-type, linking biologically-relevant information with our proposed clusters. In published proof-of-concept work, we were able to cluster seven different mouse models and make predictions about previously unknown molecular pathologies. Here, we propose to extend this work to human neurons, using primary patient cells taken from genetically sequenced autistic research subjects with identified likely causative genetic mutations, or `idiopathic' autism patients who were sequenced but no mutation was identified, or age-and-sex-matched typically developing controls. This work will reveal new, biologically relevant relationships between autisms of different known and unknown genetic etiologies, and offers the opportunity to simultaneously identify sub-groups of patients and potential drug targets that may effectively treat each identified sub-group.